What tests did you run on code from the older models? What kind of security review did it go through? When you say it was "not so good," do you mean it failed to compile or couldn't even pass basic tests?

Yes, that's why good datasets are important. But usually the difference between model generations is architectural improvements

They were trained on a lot of garbage human-written slop originally, they have to post-train the models so the responses are good. Luckily if you’re an AI company you probably have the whole chat, so it’s easy to categorise it: the user might respond “Good job, that fixed it” or “WHAT THE FUCK IS THIS??? WORST CODE I’VE EVER SEEN”.

How much do we assume human written code for training the original models is free of human made slop? We all know we all take shortcuts and have code we are not proud of. If they are I deed averaging machines, is it possible their output is the average of human output?

I'd argue that the way attention works plays into the slop too